Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations

Version 1 : Received: 10 January 2023 / Approved: 12 January 2023 / Online: 12 January 2023 (09:21:08 CET)

How to cite: Ghazikhani, H.; Butler, G. Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations. Preprints 2023, 2023010221. https://doi.org/10.20944/preprints202301.0221.v1 Ghazikhani, H.; Butler, G. Discriminating Ion Channels from Non-Ion Channel Membrane Proteins Using Machine Learning and Deep Learning Classifiers with Protein Language Model Representations. Preprints 2023, 2023010221. https://doi.org/10.20944/preprints202301.0221.v1

Abstract

Ion channels are integral membrane proteins that facilitate the movement of ions across cell membranes, playing a key role in a range of biological processes. The high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose. In our previous work, we demonstrated the effectiveness of protein language models for ion channel prediction, using a logistic regression classifier to distinguish ion channels from non-ion channels (TooT-BERT-C) and transporters from non-transporters (TooT-BERT-T). In this study, we build upon this approach by using a combination of classical machine learning classifiers and a Convolutional Neural Network (CNN) with fine-tuned representations from ProtBERT, ProtBERT-BFD, and MembraneBERT to discriminate ion channels from non-ion channels. The results of our experiments demonstrate that TooT-BERT-CNN-C, a combination of the representations from ProtBERT-BFD and a CNN, outperforms existing state-of-the-art methods for predicting ion channels, with a Matthews Correlation Coefficient (MCC) of 0.86 and an accuracy of 98.35% on an independent test set.

Keywords

Ion channels; Membrane proteins; Transmembrane proteins; Drug discovery; Protein language models; Convolutional Neural Network

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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